Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.

Abstract excerpt

Single-cell transcriptomics technologies have vast potential in advancing our understanding of cellular heterogeneity in complex tissues. While methods to interpret single-cell transcriptomics data are developing rapidly, challenges in most analysis pipeline still remain, and the major limitation is a reliance on manual annotations for cell-type identification that is time-consuming, irreproducible, and sometimes...

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